1. Field of the Invention
The present invention relates generally to wireless communication networks, and more particularly to monitoring and analyzing communication over the network.
2. Description of the Related Art
For the foreseeable future, two very important market forces may continue in wireless communication networks: the increasing complexity of wireless network services, and the increasing customer demands and competitive pressures upon wireless service providers. Viewed from the perspective of the wireless service provider, these technical and economic trends are a potent combination.
The continued growth in network load and the addition of high-speed data services combine to make the network ever more difficult to monitor, trouble-shoot, and optimize using traditional tools alone. It is important to recognize that with the addition of wireless data services, the cell loading behavior has changed in statistically significant ways. Voice users are large in number and their per-mobile communication patterns are comparatively “smooth”. Hence in a statistical sense, aggregate voice loading inherently is more self-averaging within a given cell. However data communication is much more “bursty” and the number of simultaneously served high-speed data users within a cell may be significantly smaller than for voice services. Hence fluctuations in data loading and performance are broader in character than for voice services. Additionally, data's different statistical characteristics and QOS (Quality of Service) requirements require entirely new network elements, with more complex signaling and control mechanisms throughout. Generally, these trends are apply to generic 3G wireless standards, and may be applied to any existing standard and/or future standard (i.e., these trends are not limited to CDMA2000).
The industry's challenging economic realities require service providers to continually seek avenues for shortening their time to revenue while simultaneously improving their provisioning and optimization abilities to extract the most network performance possible in order to prosper in an increasingly competitive marketplace. The collision of these technical and economic forces presents a clear challenge: to find mechanisms to achieve these economic efficiencies in the face of the growing network complexity.
Such methodologies tend to rely heavily on data regarding the performance of the network. Several network monitoring and performance monitoring techniques exist.
Traditional network Service Measurements (SMs) are typically averaged or accumulated over some measurement period, e.g., one hour. Such SMs are well suited to performance monitoring situations where the relevant quantity is deterministic and readily measurable, often associated with a particular network sub-element. An example might be the peak number of Walsh codes in use during the hour. As long as this peak demand does not exceed the maximum number of available codes, one can safely conclude that no performance degradation directly resulted. However, there also exist situations, which require comparisons or correlations between multiple quantities at a specific time within the measurement period, and these often require the SMs to be binned upon much finer time scales in order to draw statistically valid conclusions. Namely, there exist situations which require much more detailed knowledge regarding the full network state as a function of time. Two examples might be understanding and managing the interactions between simultaneous voice and data users on the same carrier, and gaining a deeper understanding regarding the confluence of events which can lead to lost calls.
Per-call service measurements are an important step along the path towards more detailed information on finer time scales, but they too have their limitations. Typically they retain only a subset of the system state and performance metrics, to avoid presenting an undue burden upon the network infrastructure.
Drive testing has long been an important tool for performance monitoring and diagnosis, and likely will continue for many years. However, there exist numerous situations where the actual user behavior is critical to understanding the network behavior and performance, something drive testing can at best only approximate. For example, data performance and provisioning are keenly dependent upon accurate packet data communication models, data user locations within the cell, the number and activity of simultaneous users, their mobility, etc. Drive testing is also relatively expensive, so collecting detailed performance data from actual user mobiles provides an important opportunity for cost reduction.